Altini Nicola, Puro Emilia, Taccogna Maria Giovanna, Marino Francescomaria, De Summa Simona, Saponaro Concetta, Mattioli Eliseo, Zito Francesco Alfredo, Bevilacqua Vitoantonio
Department of Electrical and Information Engineering (DEI), Polytechnic University of Bari, Via Edoardo Orabona n. 4, 70126 Bari, Italy.
Molecular Diagnostics and Pharmacogenetics Unit, IRCCS Istituto Tumori "Giovanni Paolo II", Via O. Flacco n. 65, 70124 Bari, Italy.
Bioengineering (Basel). 2023 Mar 23;10(4):396. doi: 10.3390/bioengineering10040396.
The segmentation and classification of cell nuclei are pivotal steps in the pipelines for the analysis of bioimages. Deep learning (DL) approaches are leading the digital pathology field in the context of nuclei detection and classification. Nevertheless, the features that are exploited by DL models to make their predictions are difficult to interpret, hindering the deployment of such methods in clinical practice. On the other hand, pathomic features can be linked to an easier description of the characteristics exploited by the classifiers for making the final predictions. Thus, in this work, we developed an explainable computer-aided diagnosis (CAD) system that can be used to support pathologists in the evaluation of tumor cellularity in breast histopathological slides. In particular, we compared an end-to-end DL approach that exploits the Mask R-CNN instance segmentation architecture with a two steps pipeline, where the features are extracted while considering the morphological and textural characteristics of the cell nuclei. Classifiers that are based on support vector machines and artificial neural networks are trained on top of these features in order to discriminate between tumor and non-tumor nuclei. Afterwards, the SHAP (Shapley additive explanations) explainable artificial intelligence technique was employed to perform a feature importance analysis, which led to an understanding of the features processed by the machine learning models for making their decisions. An expert pathologist validated the employed feature set, corroborating the clinical usability of the model. Even though the models resulting from the two-stage pipeline are slightly less accurate than those of the end-to-end approach, the interpretability of their features is clearer and may help build trust for pathologists to adopt artificial intelligence-based CAD systems in their clinical workflow. To further show the validity of the proposed approach, it has been tested on an external validation dataset, which was collected from IRCCS Istituto Tumori "Giovanni Paolo II" and made publicly available to ease research concerning the quantification of tumor cellularity.
细胞核的分割与分类是生物图像分析流程中的关键步骤。在细胞核检测与分类方面，深度学习（DL）方法引领着数字病理学领域。然而，DL模型用于进行预测的特征难以解释，这阻碍了此类方法在临床实践中的应用。另一方面，病理组学特征能够更轻松地描述分类器用于做出最终预测所利用的特征。因此，在这项工作中，我们开发了一种可解释的计算机辅助诊断（CAD）系统，可用于支持病理学家评估乳腺组织病理学切片中的肿瘤细胞密度。具体而言，我们将利用Mask R-CNN实例分割架构的端到端DL方法与一个两步流程进行了比较，在两步流程中，在考虑细胞核的形态和纹理特征的同时提取特征。基于支持向量机和人工神经网络的分类器在这些特征之上进行训练，以区分肿瘤细胞核和非肿瘤细胞核。之后，采用SHAP（Shapley值加法解释）可解释人工智能技术进行特征重要性分析，从而了解机器学习模型为做出决策而处理的特征。一位专家病理学家对所采用的特征集进行了验证，证实了该模型的临床可用性。尽管两阶段流程产生的模型准确性略低于端到端方法的模型，但其特征的可解释性更清晰，可能有助于建立病理学家在临床工作流程中采用基于人工智能的CAD系统的信心。为了进一步证明所提方法的有效性，我们在一个外部验证数据集上进行了测试，该数据集是从IRCCS Istituto Tumori “Giovanni Paolo II”收集的，并已公开提供，以方便有关肿瘤细胞密度量化的研究。
